Training may be the basis upon which desired performance may depend. Enhancement of a training scenario may be a constant and desired goal of a training entity. A trainee may perform better in a future encounter with a scenario if the trainee has had previous experience with a similar scenario. Creation of a particular scenario may be difficult and expensive to create. For example, in training a Captain of a ship, the trainee/Captain may benefit from experience with a scenario involving a second ship from which the trainee/Captain must maneuver to avoid. To create a scenario involving two ships on a collision course may be prohibitively expensive as well as unsafe for the training entity charged with education of the trainee/Captain.
Many platforms may communicate information to an operator via a data display or other visual or aural indicator. For example, the Captain of the ship may reference a radar display from which he may determine a possible course of action to take to avoid a collision. Each user interface may communicate with the user in a specific way offering data to the user beneficial to operation of the platform. The Captain may visually reference a radar display, the Captain may aurally perceive a collision warning, and the Captain may visually perceive a flashing beacon on the horizon. All of these queues may provide an input to the decision the Captain may make.
Live training scenarios may provide the best possible training environment for a trainee. A trainee who has experienced an event in a real world training environment may subsequently perform more productively than if the trainee had not experienced the training event. Actual presence in the aircraft, flying through airspace, may add an increased aspect of positive stress to the training environment. For example, a wingman may practice a maneuver to position his aircraft for weapons employment as the flight lead aircraft makes a visual identification of a target. Having performed this maneuver numerous times in an actual fighter aircraft, the wingman may perform more productively in a combat scenario than if the wingman had not previously practiced the maneuver.
Live training scenarios using actual assets may have become increasingly expensive. The cost of one flight hour of an F/A-18E Super Hornet has risen dramatically in past years.
Simulation has found success in a variety of training scenarios. The cost of simulating a scenario may be a fraction of the cost of actually creating the same Live training scenario. Quality of simulation has been enhanced recently with computer generated graphics, computer generated threats, and integrated simulations.
Simulation of information communicated to an operator may be presented to the operator via the various indicators. For example, the training entity may present the ship's Captain a simulation of a radar display on which the Captain may base a decision. This simulation may be accomplished without the use of an actual ship. Radar displays, communications heard by the trainee, threats posed to the trainee, and consequences for each action taken by the trainee may all be generated by a computer device.
A variety of prior art methods of hybrid simulation have been used to effectively integrate a Virtual entity into a Live training scenario. One example of this integration includes Live Virtual Constructive (LVC) training that has evolved to enable multi-platform integration in a training scenario. LVC may include Live assets (such as an actual aircraft flying on a weapons range), Virtual assets (such as a pilot operating a simulator at a ground-based location), and Constructive assets (those objects generated and operated by an algorithm on a computer device).
Connectivity to ensure effective presentation to an operator may pose a challenge to successful integrated LVC simulation. In order for the operator to reference a presentation, the operator must have the presentation available to him. On an aircraft, this means an effective datalink capable of transmission of data to not only a single aircraft, but to an entire large force exercise of fighter and adversary aircraft, tanker and remote sensing aircraft, ground stations, and space based assets.
A datalink capable of multi-platform and multi mission connectivity may include such factors as Quality of service (QOS), number of participants, bandwidth allocation per participant, available spectrum, and usefulness in consideration of the rules of the geographic area in which an entity may operate.
Current Datalink Protocols may be ineffective when applied to such a connectivity requirement. Prior art protocols may offer fixed sized messages, with no guarantee of message delivery. Others may offer no guarantee of delivery in the order sent, or a fixed number of slots allocated to each communicator. Some may offer package delivery in a specific order but this order guarantee may limit speed of delivery. Other protocols may offer variable size payloads with variable bandwidth requirements. These protocols may sacrifice one characteristic to perform more effectively within a second characteristic. For example, a datalink protocol may guarantee delivery of a size limited message. Most protocols maintain a limited connectivity considering all desired characteristics of the datalink. However, when communication may be a requirement for safe and effective operation, these protocols may fall short of the desired requirement.
Correlation of objects received from more than one source may add value to performance of an operator acting in reliance on the objects. A human interface display filled with uncorrelated objects may confuse a trainee/operator and distract from an otherwise valuable and expensive training scenario.
Training situations may require accurate presentations to an operator to retain training value. In some sessions, training data may be displayed alongside actual data. Uncorrelated simulated data presented alongside actual data may create an unintentional presentation. Correlation of the training data and the actual data to create a presentation as intended by a training entity may add value to the training environment.
Simulation may be one valuable tool usable by an instructor to cost effectively train a student. However, simulation data displacing actual critical data may diminish student situational awareness and lead to diminishing levels of safety. An operator confused about which data may be simulated and which data corresponds to actual events may adversely affect the level of safety of the operator and those in proximity with the operator.
Prior correlation functions may receive sensed data from a first onboard sensor and correlate with sensed data received from a similar onboard sensor on a second platform. The two platforms may correlate like data to agree on a correlated object based on various parameters e.g., proximity, altitude, speed, and location accuracy.
Prior correlation functions may lack an ability to properly correlate unlike data. For example, a radar sensor on an aircraft may provide sensed data while a simulator may provide simulated sensed data. This unlike data may provide a challenge to current correlation algorithms.
Therefore, a novel approach may provide an embedded simulator within an actual presentation available to an operator. The embedded simulation may correlate unlike data received from various sources offering an operator a clear picture of the intended presentation. This novel approach may correlate actual objects and simulated objects to offer a valuable training environment. The novel approach may further correlate data received from an onboard source with data received from an off-board source to present the best available training scenario.